To ensure these rare but important abnormal regions captured and described, the urgent problem is to alleviate such serious data deviation problem [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. In our work, we propose the Posterior-and-Prior Knowledge Exploring-and-Distilling (PPKED) framework, which imitates the radiologists' working patterns to alleviate above problems. Given a radiology image, radiologists will examine the abnormal regions and assign the disease topic tags to the abnormal regions; then accurately write a corresponding report based on years of prior medical knowledge and prior working experience accumulations [7]}, [8]}, [4]}. In order to model above working patterns, the PPKED introduces three modules, i.e., Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD). The PoKE could alleviate visual data deviation by extracting the abnormal regions based on the input image; The PrKE could alleviate textual data deviation by encoding the prior knowledge, including the prior radiology reports (i.e., prior working experience) pre-retrieved from the training corpus and the prior medical knowledge graph (i.e., prior medical knowledge), which models the domain-specific prior knowledge structure and is pre-constructed from the training corpusFor conciseness, in this paper, the prior working experience and the prior medical knowledge denote the retrieved radiology reports and the constructed medical knowledge graph, respectively.. Finally, the MKD focuses on distilling the useful knowledge to generate proper reports. As a result, as shown in Figure REF , our PPKED has higher rate of accurately describing the rare and diverse abnormalities.
